Identifying Individuals with Antisocial Personality Disorder Using Resting-State fMRI

<div><p>Antisocial personality disorder (ASPD) is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI) data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity) and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint.</p></div

Performance evaluation of the LDA+SVM classifier.

<p>(a) The curve of the generalization rate to the number of features. (b) Permutation distribution of the estimate (repetition times: 10,000). GR0 is the generation rate obtained by the classifier trained on the real class labels. With the generalization rate statistic, this figure reveals that the classifier learned the relationship between the data and the labels with a probability of being wrong of <0.0001.</p

Altered resting-state functional connectivity and networks in individuals with antisocial personality disorder.

<p>Altered resting-state functional connectivity and networks in individuals with antisocial personality disorder.</p

Brain regions with high discriminative power.

<p>Brain regions with high discriminative power.</p

Flow chart of the LDA+SVM classifier.

<p>Flow chart of the LDA+SVM classifier.</p

Altered resting-state functional connectivity in ASPD.

<p>(a) τ value distribution of all 48 features represented in the LOOCV. The horizontal axis represents each functional connection and the vertical axis represents the weighted Kendall tau correlation coefficient. (b) Region weights and the distribution of the 20 high discriminative power functional connections. Regions are color-coded by category, red sphere represented default mode network, green sphere represented attentional network, brown sphere represented cerebellum.</p

Comparison of the classification performance of different multivariate pattern classifiers.

<p>LLE, locally linear embedding; LDA, linear discriminant analysis; PCA, principal component analysis; SVM, Support Vector Machine; GR, generalization rate; SS, sensitivity; SC specificity.</p

Characteristics of the participants in this study.

<p>ASPD: offenders with antisocial personality disorder.</p

Brain networks weights.

<p>(a): Summarized weights for each of the seven communities. (b):The sums of the functional connection weights between the networks. RSN1: default mode network, RSN2: attention network, RSN3, visual recognition network, RSN4: auditory network, RSN5: sensory-motor areas, RSN6: subcortical network, RSN7: cerebellum network.</p